Multiresolutional critical point filter and image matching using the same

ABSTRACT

A multiresolutional filter called a critical point filter is introduced. This filter extracts a maximum, a minimum, and two types of saddle points of pixel intensity for every 2×2 (horizontal x vertical) pixels so that an image of a lower level of resolution is newly generated for every type of a critical point. Using this multiresolutional filter, a source image and a destination image are hierarchized, and source hierarchical images and destination hierarchical images are matched using image characteristics recognized through a filtering operation.

BACKGROUND OF THE INVENTION

1. Field of the Invention

This invention relates to a multiresolutional filter which generateshierarchical images. In particular, this invention relates to a methodfor generating an image having a lower resolution using amultiresolutional filter, and an image matching method capable of usingthis filtering method.

2. Description of the Related Art

Automatic matching of two images, that is, correspondence between imageregions or pixels, has been one of the most important and difficultthemes of computer vision and computer graphics. For instance, once theimages of an object from different view angles are matched, they can beused as the base for generating other views. When the matching ofright-eye and left-eye images is computed, the result can immediately beused for stereo photogrammetry. When a model facial image is matchedwith another facial image, it can be used to extract characteristicfacial parts such as the eyes, the nose, and the mouth. When two imagesof, for example, a man and a cat are matched exactly, all the in-betweenimages can be generated and hence morphing can be done fullyautomatically.

However, in the existing methods, the correspondence of the points ofthe two images must generally be specified manually, which is a tediousprocess. In order to solve this problem, various methods forautomatically detecting correspondence of points have been proposed. Forinstance, application of an epipolar line has been suggested to reducethe number of candidate pairs of points, but the complexity is high. Toreduce the complexity, the coordinate values of a point in the left-eyeimage are usually assumed to be close to those of the correspondingpoint in the right-eye image. Providing such restriction, however, makesit very difficult to simultaneously match global and localcharacteristics.

In volume rendering, a series of cross-sectional images are used forconstituting voxels. In such a case, conventionally, it is assumed thata pixel in the upper cross-sectional image correspond to the pixel thatoccupies the same position in the lower cross section, and this pair ofpixels is used for the interpolation. Using this very simple method,volume rendering tends to suffer from unclear reconstruction of objectswhen the distance between consecutive cross sections is long and theshape of the cross sections of the objects thus changes widely.

A great number of image matching algorithms such as the stereophotogrammetry methods use edge detection. In such a method, however,the resulting matched pairs of points are sparse. To fill the gapsbetween the matched points, the disparity values are interpolated. Ingeneral, all edge detectors suffer from the problem of judging whether achange in the pixel intensity in a local window they use really suggeststhe existence of an edge. These edge detectors suffer from noisesbecause all edge detectors are high pass filters by nature and hencedetect noises at the same time.

Optical flow is another known method. Given two images, optical flowdetects the motion of objects (rigid bodies) in the images. It assumesthat the intensity of each pixel of the objects does not change andcomputes the motion vector (u,v) of each pixel together with someadditional conditions such as the smoothness of the vector field of(u,v). Optical flow, however, cannot detect the global correspondencebetween images because it concerns only the local change of pixelintensity and systematic errors are conspicuous when the displacementsare large.

To recognize the global structures, a great number of multiresolutionalfilters have been proposed. They are classified into two groups: linearfilters and nonlinear filters. An example of the former is a wavelet.However, the linear filters are not useful when used for image matching,because the information of the pixel intensity of extrema as well astheir locations are blurred. FIGS. 1(a) and 1(b) show the result of theapplication of an averaging filter to the facial images in FIGS. 19(a)and 19(b), respectively. FIGS. 1(k)-1(l) show the results of theapplication of the scaling function of the Battle-Lemarie wavelet to thesame facial images. As shown in these drawings, the pixel intensity ofextrema is reduced through averaging while the locations are undesirablyshifted due to the influence of averaging. As a result, the informationof the locations of the eyes (minima of the intensity) is ambiguous atthis coarse level of resolution and hence it is impossible to computethe correct matching at this level of resolution. Therefore, although acoarse level is prepared for the purpose of global matching, theobtained global matching does not correctly match the truecharacteristics of the images (eyes, i.e., the minima) correctly. Evenwhen the eyes appear clearly at the finer level of resolution, it is toolate to take back the errors introduced in the global matching. Bysmoothing the input images, stereo information in textured regions isalso filtered out as pointed out.

On the other hand, 1D sieve operators have become available as nonlinearfilters which can be used for morphological operations. 1D sieveoperators smooth out the images while preserving scale-space causalityby choosing the minimum (or the maximum) inside a window of a certainsize. The resulting image is of the same size as the original, but issimpler because small undulations are removed. Although this operatormay be classified as "a multiresolutional filter" in a broad sense thatit reduces image information, it is not a multiresolutional filter in anormal sense as it does not put images into hierarchy while changing theresolution of the images as wavelets do. This operator thus cannot beutilized for detection of correspondence between images.

SUMMARY OF THE INVENTION

In view of the above, the following problems are presented.

1. Image processing methods have rarely been available for accuratelyidentifying the characteristics of an image through relatively simpleprocessing. In particular, effective proposals have been scarcely madein connection with a method for extracting characteristics of an imagewhile preserving information, such as the pixel value or location of acharacteristic point.

2. Automatic detection of a corresponding point based on thecharacteristics of an image generally has had problems including complexprocessing and low noise durability. In addition, various restrictionshave been necessarily imposed in processing, and it has been difficultto obtain a matching which satisfies global and local characteristics atthe same time.

3. Although a multiresolutional filter is introduced for recognition ofthe global structure or characteristics of an image, in the case of alinear filter, information regarding the intensity and location of apixel becomes blurred. As a result, corresponding points can hardly berecognized with sufficient accuracy. In addition, the 1D sieve operator,which is a non-linear filter, does not hierarchize an image, and cannotbe used for detection of a corresponding point between images.

4. With the above problems, extensive manual labor has been inevitablyrequired in processing in order to accurately obtain correspondingpoints.

The present invention has been conceived to overcome the above problems,and aims to provide techniques for allowing accurate recognition ofimage characteristics in the image processing field.

In one aspect of the present invention, a new multiresolutional imagefilter is proposed. This filter is called a critical point filter as itextracts a critical point from an image. A critical point stands for apoint having a certain characteristic in an image, including a maximum,where a pixel value (that is, an arbitrary value for an image or apixel, such as a color number or the intensity) becomes maximum in acertain region, a minimum, where it becomes minimum, and a saddle point,where it becomes maximum for one direction and minimum for another. Acritical point may be based on a topological concept, but it may possessany other characteristics. Selection of criteria for a critical point isnot an essential matter in this invention.

In the above aspect, image processing using a multiresolutional filteris carried out. In a detection step, a two dimensional search isperformed on a first image to detect a critical point. In a followinggeneration step, the detected critical point is extracted for generationof a second image having a lower resolution than that of the firstimage. The second image inherits critical points from the first image.The second image, having a lower resolution than the first image, ispreferably used for recognition of global characteristics of an image.

Another aspect of the invention relates to an image matching methodusing a critical point filter. In this aspect, source and destinationimages are matched. The terms a source image" and "a destination image"are determined only for a discriminating purpose, and there is noessential difference between them.

In a first step of this aspect, a critical point filter is applied to asource image to generate a series of source hierarchical images eachhaving a different resolution. In a second step, a critical point filteris applied to a destination image to generate a series of destinationhierarchical images. Source and destination hierarchical images standfor a group of images which are obtained by hierarchizing source anddestination images, respectively, and each consist of two or moreimages. In a third step, matching between source and destinationhierarchical images is computed. In this aspect, image characteristicsconcerning a critical point are extracted and/or clarified using amultiresolutional filter. This facilitates matching. According to thisaspect, matching may be totally unconstrained.

Still another aspect of the present invention relates to matching sourceand destination images. In this aspect, an evaluation equation is setbeforehand for each of a plurality of matching evaluation items; theseequations are combined into a combined evaluation equation; and anoptimal matching is detected while paying attention to the neighborhoodof an extreme of the combined evaluation equation. A combined evaluationequation may be defined as a linear combination or a sum of theseevaluation equations, at least one of which has been multiplied by acoefficient parameter. In such a case, the parameter may be determinedby detecting the neighborhood of an extreme of the combined evaluationequation or any of the evaluation equation. The above description usedthe term "the neighborhood of an extreme," because some error istolerable as it does not seriously affect the present invention.

Since an extreme itself depends on the parameter, it becomes possible todetermine an optical parameter based on the behavior of an extreme.Automatic determination of a parameter, which originally accompaniesdifficulties in tuning, is achieved.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and the other objects, features, and advantages, will becomefurther apparent from the following description of the preferredembodiment taken in conjunction with the accompanying drawings wherein:

FIG. 1(a) shows an image obtained as the result of the application of anaveraging filter to one human facial image;

FIG. 1(b) shows an image obtained as the result of the application of anaveraging filter to another human facial image;

FIG. 1(c) shows an image of one human face at p.sup.(5,0) obtained in apreferred embodiment;

FIG. 1(d) shows an image of another human face at p.sup.(5,0) obtainedin a preferred embodiment;

FIG. 1(e) shows an image of one human face at p.sup.(5,1) obtained in apreferred embodiment;

FIG. 1(f) shows an image of one human face at p.sup.(5,1) obtained in apreferred embodiment;

FIG. 1(g) shows an image of one human face at p.sup.(5,2) obtained in apreferred embodiment;

FIG. 1(h) shows an image of one human face at p.sup.(5,2) obtained in apreferred embodiment;

FIG. 1(i) shows an image of one human face at p.sup.(5,3) obtained in apreferred embodiment;

FIG. 1(j) shows an image of one human face at p.sup.(5,3) obtained in apreferred embodiment;

FIG. 1(k) shows an image obtained as the result of the application ofthe scaling function of the Battle-Lemarie wavelet to one human facialimage;

FIG. 1(l) shows an image obtained as the result of the application ofthe scaling function of the Battle-Lemarie wavelet to another facialimage;

FIG. 2(R) shows an original quadrilateral;

FIG. 2(A) shows an inherited quadrilateral;

FIG. 2(B) shows an inherited quadrilateral;

FIG. 2(C) shows an inherited quadrilateral;

FIG. 2(D) shows an inherited quadrilateral;

FIG. 2(E) shows an inherited quadrilateral;

FIG. 3 is a diagram showing the relationship between a source image anda destination image and that between the m-th level and the (m-1)thlevel, using a quadrilateral;

FIG. 4 shows the relationship between a parameter η and energy Cf;

FIG. 5(a) is a diagram illustrating determination of whether or not themapping for a certain point satisfies Bijectivity conditions throughouter product computation;

FIG. 5(b) is a diagram illustrating determination of whether or not themapping for a certain point satisfies Bijectivity conditions throughouter product computation;

FIG. 6 is a flowchart of the entire procedure of a preferred embodiment;

FIG. 7 is a flowchart showing the details of the process at S1 in FIG.6;

FIG. 8 is a flowchart showing the details of the process at S10 in FIG.7;

FIG. 9 is a diagram showing correspondence between partial images of them-th and (m-1)th levels of resolution;

FIG. 10 is a diagram showing source hierarchical images generated in theembodiment;

FIG. 11 is a flowchart of a preparation procedure for S2 in FIG. 6;

FIG. 12 is a flowchart showing the details of the process at S2 in FIG.6;

FIG. 13 is a diagram showing the way a submapping is determined at the0-th level;

FIG. 14 is a diagram showing the way a submapping is determined at thefirst level;

FIG. 15 is a flowchart showing the details of the process at S21 in FIG.12;

FIG. 16 is a diagram showing the behavior of energy C.sup.(m,s) fcorresponding to f.sup.(m,s) (λ=iΔλ) which has been obtained for acertain f.sup.(m,s) while changing λ;

FIG. 17 is a diagram showing the behavior of energy C.sup.(n) fcorresponding to f.sup.(n) (η=iΔη) (i=0, 1, . . . ) which has beenobtained while changing η;

FIG. 18(a) shows a left-eye image of an object;

FIG. 18(b) shows a right-eye image of an object;

FIG. 18(c) shows an interpolation image of an object, generated in thepreferred embodiment;

FIG. 19(a) shows an image of one human face;

FIG. 19(b) shows an image of another human face;

FIG. 19(c) shows an image of the human faces shown in FIGS. 19(a) and19(b) superimposed on each other;

FIG. 19(d) shows a morphing image generated in the preferred embodiment;

FIG. 20(a) shows the face of a cat;

FIG. 20(b) shows a morphing image of the face of a cat and a human face;

FIG. 21(a) shows a left-eye image including many objects;

FIG. 21(b) shows a right-eye image including many objects;

FIG. 21(c) shows an interpolation image including many objects,generated in the preferred embodiment;

FIG. 22(a) shows an MRI source image;

FIG. 22(b) shows an MRI destination image;

FIG. 22(c) shows an interpolation image generated in the preferredembodiment; and

FIG. 22(d) shows a volume rendering image generated based on aninterpolation image.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

Elemental techniques applied in a preferred embodiment will be firstdescribed in [1]. A concrete description of a processing procedure willthen be given in [2], and experimental results will be reported in [3].

[1] Detailed Description of Elementary Techniques

[1.1] Introduction

Using a set of new multiresolutional filters called critical pointfilters, image matching is accurately computed. There is no need for anyprior knowledge of the objects. The matching of the images is computedat each resolution while going down the resolution hierarchy generallyfrom the coarse level to the fine level. Parameters necessary for thecomputation are set completely automatically by dynamical computationanalogous to human visual systems. There is no need of manuallyspecifying correspondence of points between images.

This embodiment can be applied to, for instance, completely automatedmorphing, object recognition, stereo photogrammetry, volume rendering,smooth generation of motion images from a small number of frames. Whenapplied to morphing, given images can be automatically transformed. Whenapplied to volume rendering, intermediate images between cross sectionscan be accurately reconstructed, even when the distance between them islong and the cross sections vary widely in shape.

[1.2] The Hierarchy of the Critical Point Filters

Multiresolutional filters according to this embodiment preserve theintensity and the locations of each critical point of the images whilereducing the resolution at the same time. Let the width of the image beN and the height be M. For simplicity, N=M=2^(n) is assumed wherein n isa positive integer. An interval [0,N].OR right.R is denoted by I. Apixel of the image at the location (i,j) is denoted by p.sup.(i,j)wherein i,j.di-elect cons.I.

A multiresolution hierarchy is introduced. Hierarchized image groups areconstructed using a multiresolutional filter. The multiresolutionalfilter carries out a two dimensional search on an original image anddetects critical points. It then extracts the critical points toconstruct another image having a lower resolution. The size of each ofthe respective images of the m-th level can be denoted as 2^(m) ×2^(m)(0≦m≦n). The critical point filter constructs the following four newhierarchical images recursively. ##EQU1## These four images arehereinafter referred to as subimages. When min x≦t≦x+1, max x≦t≦x+1 areabbreviated to α and β, the subimages can be abbreviated to thefollowings.

    p.sup.(m,0) =α(x)α(y)p.sup.(m+1,0)

    p.sup.(m,1) =α(x)β(y)p.sup.(m+1,1)

    p.sup.(m,2) =α(x)α(y)p.sup.(m+1,2)

    p.sup.(m,3) =α(x)β(y)p.sup.(m+1,3)

That is, they are analogous to the tensor products of α and β. Subimagescorrespond to different kinds of critical points, respectively. As isapparent from the above equations, a critical point filter detects acritical point of the original image for every block constituting 2×2pixels. In this detection, a point having a maximum pixel value and apoint having a minimum pixel value are searched with respect to twodirections, i.e., vertical and horizontal directions, in each block.Although pixel intensity is employed as a pixel value in thisembodiment, various other values relative to an image may be employed. Apixel having maximum pixel values for the two directions, one havingminimum pixel values for the two directions, one having a minimum pixelvalue for one direction and a maximum pixel value for the otherdirection are detected as a maximum, a minimum, and a saddle point,respectively.

A critical point filter makes a one pixel image of a critical pointdetected inside each of the respective blocks represent the image (fourpixels here) of the block, to thereby reduce resolution of the image.From the singularity theoretical point of view, α(x)α(y) preserves theminima, β(x)β(y) preserves the maxima, α(x)β(y) and β(x)α(y) preservethe saddle points.

In operation, at the beginning, critical point filtering is appliedseparately to a source image and a destination image so as to generate aseries of image groups, i.e., source hierarchical images and destinationhierarchical images. Four source hierarchical images and fourdestination hierarchical images are generated corresponding to fourcritical point types. Then, source hierarchical images and destinationhierarchical images are matched. The minima are first matched usingp.sup.(m,0), the saddle points are then matched using p.sup.(m,1) basedon the previous matching result for the minima, other saddle points arematched using p.sup.(m,2), and finally the maxima are matched usingp.sup.(m,3).

FIGS. 1(c) and 1(d) show the subimages p.sup.(5,0) of the images inFIGS. 1(a) and 1(b), respectively. Similarly, FIGS. 1(e) and 1(f) showthe subimages p.sup.(5,1) ; FIGS. 1(g) and 1(h) show the subimagesp.sup.(5,2) ; and FIGS. 1(i) and 1(j) show the subimages p.sup.(5,3).Characteristics in images can be easily matched using subimages. Theeyes can be matched by p.sup.(5,0) because the eyes are minima of pixelintensity in a face. The mouths can be matched by p.sup.(5,1) becausethe mouths have low intensity in the horizontal direction. Verticallines on the both sides of the necks become clear by p.sup.(5,2). Theears and bright parts of cheeks become clear by p.sup.(5,3) becausethese are maxima of pixel intensity.

As described above, characteristics of an image can be extracted ordistinguished a critical point filter. Comparison betweencharacteristics of an image shot by a camera and of several objectsstored beforehand enables to recognize an object shot by a camera.

[1.3] Computation of Mapping Between Images

The pixel of the source image at the location (i,j) is denoted byp.sup.(n) (i,j) and that of the destination image at (k,l) is denoted byq.sup.(n) (i,j) where i, j, k, l.di-elect cons.I. The energy of themapping between the images (described later) is then defined. Thisenergy is determined by the difference in the intensity of the pixel ofthe source image and its corresponding pixel of the destination imageand by the smoothness of the mapping. First, the mapping f.sup.(m,0):p.sup.(m,0) →q.sup.(m,0) between p.sup.(m,0) and q.sup.(m,0) with theminimum energy is computed. Based on f.sup.(m,0), the mappingf.sup.(m,1) between.sup.(m,1) and q.sup.(m,1) with the minimum energy iscomputed. This process continues until f.sup.(m,3) between p.sup.(m,3)and q.sup.(m,3) is computed. Each f.sup.(m,i) (i=0, 1, 2, . . . ) isreferred to as a submapping. The order of i will be changed as followsin computing f.sup.(m,i) for reasons to be described later.

    f.sup.(m,i) :p.sup.(m,σ(i)) →q.sup.(m,σ(i))

wherein σ(i).di-elect cons.{0, 1, 2, 3}.

[1.3.1] Bijectivity

When the matching between a source image and a destination image isexpressed by means of a mapping, that mapping should satisfy theBijectivity Conditions (BC) between the two images because respectiveimages should be connected satisfying both surjection and injection asthere is no conceptual supremacy existing between these images. Itshould be noted, however, that the mappings to be constructed here arethe digital version of the bijection. In this embodiment, a pixel isidentified with a grip point.

The mapping of the source subimage (a subimage of a source image) to thedestination subimage (a subimage of a destination image) is representedby f.sup.(m,s) : I/2^(n-m) ×I/2^(n-m) →I/2^(n-m) ×I/2^(n-m) (s=0, 1, . .. ), wherein f.sup.(m,s) (i,j)=(k,l) means that p.sup.(m,s).sub.(i,j) ofthe source image is mapped to q.sup.(m,s) (k,l) of the destinationimage. For simplicity, a pixel q.sub.(k,l) where f(i,j)=(k,l) holds isdenoted by q_(f)(i,j).

The definition of Bijectivity is not trivial when the data sets arediscrete as image pixels (grip points) in this embodiment. The bijectionin this case will be defined as follows, wherein i, i, j, j , k, and lare integers. First, each square ##EQU2## on the source image planedenoted by R is considered, wherein i=0 . . . 2^(m) -1, and j=0, . . . ,2^(m) -1. The edges of R are directed as the following. ##EQU3##

It is necessary that the square be mapped by f to a quadrilateral on thedestination image plane. The quadrilateral ##EQU4## denoted byf.sup.(m,s) (R) should satisfy the following Bijectivity conditions.

1. The edges of the quadrilateral f.sup.(m,s) should not intersect oneanother.

2. The orientation of the edges of f.sup.(m,s) (R) should be the same asthat of R (clockwise for FIG. 2).

3. For relaxation, mappings that are retractions are allowed.

Without relaxation, there would be no mappings which completely satisfythe BC other than an identity mapping. In this embodiment, the length ofone edge of f.sup.(m,s) (R) may be zero. That is, f.sup.(m,s) (R) may bea triangle here. However, it must not be a point or a line segmenthaving no area in this embodiment. If FIG. 2(R) is the originalquadrilateral, FIGS. 2(A) and 2(D) satisfy BC, but FIGS. 2(B), 2(C), and2(E) do not.

In implementation, the following condition may be imposed to easeinsuring that the mapping is surjection; each pixel on the boundary ofthe source image is mapped to the pixel that occupies the same locationat the destination image, i.e., f(i,j)=(i,j) (on the four lines of i=0,i=2^(m) -1, j=0, j=2^(m) -1). This condition is hereinafter abbreviatedto an additional condition.

[1.3.2] The Energy of Mapping

[1.3.2.1] Cost Related to the Pixel Intensity

The energy of the mapping f is defined. A mapping whose energy isminimum will be searched. The energy is determined mainly by thedifference in the intensity of the pixel of the source image and itscorresponding pixel of the destination image. That is, the energyC.sup.(m,s).sub.(i,j) of the mapping f.sup.(m,s) at (i,j) is determinedas ##EQU5## wherein V(p.sup.(m,s).sub.(i,j)) and V(q.sup.(m,s)_(f)(i,j))are the intensity values of the pixels p(m,s).sub.(i,j) andq.sup.(m,s)_(f)(i,j), respectively. The total energy C.sup.(m,s) of f isa matching evaluation equation, and can be defined as the sum ofC.sup.(m,s).sub.(i,j) ; i.e., ##EQU6##

[1.3.2.2] Cost Related to the Locations of the Pixel for Smooth Mapping

To obtain smooth mappings, another energy D_(f) of the mappings isintroduced. This energy is determined by the locations ofp.sup.(m,s).sub.(i,j) and q.sup.(m,s) f.sub.(i,j) (i=0, . . . , 2^(m)-1, j=0, . . . , 2^(m) -1), regardless of the intensity of the pixels.The energy D.sup.(m,s).sub.(i,j) of the mapping f.sup.(m,s) at (i,j) isdetermined as ##EQU7## wherein coefficient parameter η0 is a realnumber, and ##EQU8## and f(i,j ) is defined to be zero for i<0 and j<0.E₀ is determined by the distance between (i,j) and f(i,j). E₀ prevents apixel being mapped to a pixel too distant. It will be replaced later byanother energy function. El ensures the smoothness of the mapping. Itrepresents the distance between the displacement of p.sub.(i,j) and thedisplacements of its neighbors. Based on the above, another equation for##EQU9##

[1.3.2.3] Total Energy of the Mapping

The total energy of the mapping, i.e., a combined evaluation equation isdefined as λC.sup.(m,s)_(f) +D.sup.(m,s)_(f), wherein λ≧0 is a realnumber. The goal is to detect an extreme of the combined evaluationequation, that is, to find a mapping that gives the minimum energydenoted by ##EQU10##

Note that the mapping is an identity mapping (i.e., f.sup.(m,s)(i,j)=(i,j) for all i=0, . . . , 2^(m) -1 and j=0, . . . , 2^(m) -1)when λ=0 and η=0. As described later, the mapping will be graduallymodified or transformed from an identity mapping because the case of λ=0and η=0 is initially evaluated in this embodiment. If a combinedevaluation equation is assumed as C.sup.(m,s)_(f) +λD.sup.(m,s)_(f),with the position of λ being changed, the equation where λ=0 and η=0 isdenoted simply by C.sup.(m,s)_(f) and pixels would be randomlycorresponded to each other only because their pixel intensities areclose. This makes the mapping totally meaningless. Modifying mappingsbased on such a meaningless mapping makes no sense. Thus, a coefficientparameter is determined such that an identity mapping is initiallyselected for the evaluation as the best mapping.

Similar to this embodiment, optical flow considers the difference in thepixel intensity and smoothness. However, it cannot be used for imagetransformation because it takes into account only local movement of anobject. Using a critical point filter according to this embodiment,global correspondence can be detected.

[1.3.3] Determining the Mapping with Multiresolution

A mapping f_(min) that gives the minimum energy and satisfies BC issearched using the multiresolution hierarchy. The mappings between thesource and destination images at each level of the resolution iscomputed. Starting from the coarsest image of the top of the resolutionhierarchy, the mapping is determined at each level, while consideringthe mapping at other levels. The number of candidate mappings at eachlevel is constrained by using the mappings at an upper, i.e., coarser,level of the hierarchy. To be more specific, a mapping at a certainlevel is determined using the mapping obtained at the coarser level byone. When ##EQU11## holds, p.sup.(m-1,s).sub.(i',j') andq.sup.(m-1,s).sub.(i',j') are respectively called the parent ofp.sup.(m,s).sub.(i,j) and q.sup.(m,s).sub.(i,j). Note that [x] is thelargest integer that does not exceed x. Conversely,p.sup.(m,s).sub.(i,j) and q.sup.(m,s).sub.(i,j) are called the child ofp.sup.(m-1,s).sub.(i,j) and q.sup.(m-1,s).sub.(i,j), respectively. Thefunction parent (i,j) is defined as ##EQU12##

A mapping f.sup.(m,s) between p.sup.(m,s).sub.(i,j) andq.sup.(m,s).sub.(k,l) is then determined by computing and finding theminimum energy. The value f.sup.(m,s) (i,j)=(k,l) is determined asfollows using f.sup.(m-1,s) (m=1, 2, . . . , n). Imposing a conditionthat q.sup.(m,s).sub.(k,j) should be inside a quadrilateral definedbelow, the mappings which are thought to be reasonable or natural areselected from among the mappings which satisfy the BC. ##EQU13##

The quadrilateral defined above is hereinafter referred to as theinherited quadrilateral of p.sup.(m,s).sub.(i,j). Inside the inheritedquadrilateral, the pixel that minimizes the energy is searched.

The above description is illustrated in FIG. 3, wherein the pixels A, B,C, and D of the source image are mapped to A, B, C, and D of thedestination image, respectively, at the (m-1)th level in the hierarchy.The pixel p.sup.(m,s).sub.(i,j) should be mapped to the pixelq.sup.(m,s)_(f)(m)(i,j) that exists in the interior of the inheritedquadrilateral A'B'C'D'. With the above consideration, bridging from themapping at the (m-1)th level to that at the m-th level is achieved.

The energy E₀ defined above is now replaced by

    E.sub.0.sbsb.(i,j) =∥f.sup.(m,0) (i,j)-g.sub.(m) (i,j)∥

for computing the submapping f.sup.(m,0), and

    E.sub.0.sbsb.(i,j) =∥f.sup.(m,s) (i,j)-f.sup.(m,s-1) (i,j)∥.sup.2 (1≦i)                        (20)

for computing the submapping f.sup.(m,s) at the m-th level.

In this way, a mapping that maintains a low energy for all thesubmappings is obtained. Equation 20 makes submappings for differentcritical points associated to each other in the manner that subimages atthe same level have high similarity. Equation 19 represents the distancebetween f.sup.(m,s) (i,j) and the location where (i,j) should be mappedwhen regarded as a part of a pixel at the (m-1)th level.

Where there is no pixel that satisfies the BC inside the inheritedquadrilateral A'B'C'D', the following measures are employed. First,pixels whose distance from the boundary of A'B'C'D' is L (at first, L=1)are examined. Among them, the one with the minimum energy satisfying theBC is chosen as the value of f.sup.(m,s) (i,j). L is increased untilsuch a pixel is found or L reaches its upper bound L.sup.(m) max.L.sup.(m) max is fixed for each level m. If no such a pixel is found atall, the third condition of the BC is abandoned temporarily and suchmappings that cause the quadrilateral to become a point or a line arepermitted for determining f.sup.(m,s).sub.(i,j). If such a pixel isstill not found, the first and the second conditions of the BC are nextabandoned.

Multiresolution approximation is essential for determining the globalcorrespondence of the images while avoiding the mapping being affectedby small details of the images. Without the multiresolutionapproximation, it is impossible to detect a correspondence betweenpixels whose distance is large and hence the size of an image is limitedto be very small, and only tiny changes in this images can be handled.Moreover, imposing smoothness on the mapping prevents finding thecorrespondence of such pixels because the energy of the mapping from apixel to a pixel at a distance is high. The multiresolutionapproximation enables finding the appropriate correspondence of suchpixels because the distance between them is small at the upper (coarse)level of the hierarchy of the resolution.

[1.4] Automatic Determination of the Optimal Parameter Values

One of the main deficiencies of existing image matching techniques liesin the difficulty of parameter tuning. In most cases, the tuning is donemanually and it has been extremely difficult to choose the optimalvalue. According to this embodiment, the optimal parameter values can beobtained completely automatically.

The system of this embodiment includes two parameters, namely, λ and η.λ is the weight of the difference of the pixel intensity and ηrepresents the stiffness of the mapping. These parameter values areincreased step by step starting from 0. λ is first increased whilefixing η at 0. As λ gets larger from 0 and the value of the combinedevaluation equation (equation 14) is minimized, the value ofC.sup.(m,s)_(f) for each submapping generally becomes smaller. Thisbasically means that the two images are matched better. However, if thevalue λ exceeds its optimal value, the following phenomena occur.

1. Pixels which should not be corresponded are erroneously correspondedonly because their intensities are close.

2. Correspondence between images becomes inaccurate, and the mapping isspoiled.

3. D.sup.(m,s)_(f) in equation 14 is going to increase abruptly.

4. The value of equation 14 is going to increase abruptly, sof.sup.(m,s) is so changed that the abrupt increase of D.sup.(m,s)_(f) issuppressed. This resultantly causes an increase of C.sup.(m,s)_(f).

Therefore, a threshold value at which C.sup.(m,s)_(f) is turned toincrease is detected while tracing the minimum value of equation 14, andthe detected value is determined as the optimal value of λ at η=0.Subsequently, the behavior of C.sup.(m,s)_(f) is observed whileincreasing η step by step, and η is automatically determined accordingto a procedure described later. Corresponding to η thus determined, λ isalso determined.

This method is analogous to the focusing mechanism of human visualsystems where the images of the left and right eye are matched whilemoving one eye and the eye is fixed when the objects are clearlyrecognized.

[1.4.1] Dynamic Determination of λ

λ is increased from 0 step by step, and each time λ is changed asubmapping is evaluated. As is shown by equation 14, the total energy isdefined by λC.sup.(m,s)_(f) +D.sup.(m,s)_(f). D.sup.(m,s)_(f) inequation 9 represents the smoothness and theoretically becomes minimumfor an identity mapping which is examined first. E₀ and E₁ increase whenthe mapping is changed because it becomes more distorted from theidentity mapping. As E₁ is an integer, 1 is the smallest step ofD.sup.(m,s)_(f). Thus, it is impossible to reduce the total energy bychanging the mapping, unless the new mapping reducesλC.sup.(m,s).sub.(i,j) by one or more.

In this situation, it is now shown that condition, C.sup.(m,s).sub.(i,j)decreases in normal cases as λ increases. The histogram ofC.sup.(m,s).sub.(i,j) is denoted as h(l), wherein h(l) is the number ofpixels whose energy C.sup.(m,s).sub.(i,j) is l². In order that λl ² ≧1holds, the case of l² =1/λ is now considered. When λ is slightly variedfrom λ₁ to λ₂, a number of pixels expressed by ##EQU14## move to a morestable state having the energy of ##EQU15## Here, all the energy ofthese pixels are assumed to become zero. This equation represents thatthe value of C.sup.(m,s)_(f) is changed by ##EQU16## holds.

As h(l)>0, C.sup.(m,s)_(f) decreases in normal cases. When λ tries to gobeyond the optimal value, however, the above phenomenon, that is, anincrease of C.sup.(m,s)_(f) occurrs. By detecting this phenomenon, theoptimal value of λ is determined.

If ##EQU17## is assumed wherein H>0 and k are constants, ##EQU18##holds. If k≠-3, ##EQU19## holds. This is a generalized equation forC.sup.(m,s)_(f), wherein C is a constant.

In detection of the optimal value for λ, the number of pixels violatingthe BC may be checked for safety. The probability of violating the BCwhen determining a mapping for each pixel is assumed as p₀. As ##EQU20##holds, the number of pixels violating the BC increases at the rate##EQU21## and hence ##EQU22## is a constant. If h(l)=H1^(k) is assumed,for example

    B.sub.0 λ.sup.3/2+k/2 =p.sub.0 H                    (31)

is a constant. When λ goes beyond the optimal value, however, the abovevalue increases abruptly. This phenomenon is detected to determine theoptimal value of λ by checking whether or not B₀ λ^(3/2+k/2) /2^(m)exceeds an abnormal value B_(0thres). The increasing rate B₁ of pixelsviolating the third condition of the BC is checked in the same way bychecking whether or not B₁ λ^(3/2+k/2) /2^(m) exceeds an abnormal valueB_(1thres). The reason why the factor 2^(m) is introduced is describedlater. The system is not sensitive to the two threshold values. They areused to detect abnormally excessive distortion of the mapping in case itis failed to be detected through observation of the energy valueC.sup.(m,s)_(f).

In the implementation, if λ exceeds 0.1 when computing the submappingf.sup.(m,s), the computation of f.sup.(m,s) is abandoned and thecomputation of f.sup.(m,s+1) is started. This is because computation ofsubmappings is affected by the difference of mere 3 out of 256 levels inthe pixel intensity when λ>0.1, and it is then difficult to get acorrect result.

[1.4.2] The Histogram h(l)

Checking of C.sup.(m,s)_(f) does not depend on the histogram h(l), whilechecking of BC and the third condition of BC may depend on h(l).Actually, k is typically around 1 if (λ,C.sup.(m,s)_(f)) is plotted. Inthe implementation, k=1 is used, and B₀ λ² and B₁ λ² are checked. If thetrue value of k is smaller than 1, B₀ λ¹ and B₁ λ² are not constants andincrease gradually by the factor λ.sup.(1-k)/2. When h(l) is a constant,the factor is, for instance, λ^(1/2). Such a difference can be absorbedby setting the threshold B_(0thres) appropriately.

Here, model the source image by a circular object with its center at(x₀,y₀) and radius r given by ##EQU23## and the destination image by##EQU24## with its center at (x₁,y₁) and radius r. Let c(x) be in theform of c(x)=x^(k). The histogram h(l) is then in the form of

    h(l)∝rl.sup.k (k≠0)                           (34)

if the centers (x₀,y₀) and (x₁,y¹) are sufficiently far.

When k=1, the images represent objects with clear boundaries embedded inthe backgrounds. These objects are darkest at their centers andbrightest at their boundaries. When k=-1, the images represent objectswith vague boundaries. These objects are brightest at their centers, anddarkest at their boundaries. It is generally considered that normalobjects are usually in-between of these two types. Therefore, k being-1≦k≦1 can cover the most cases, and it is ensured that equation 27 is adecreasing function for this range.

As is known from equation 34, r is affected by the resolution of theimages; i.e., r is proportional to 2^(m). This is the reason why thefactor 2^(m) is introduced in [1.4.1].

[1.4.3] Dynamic Determination of η

The other parameter η can also be determined automatically in the samemanner. Initially, η=0 is set, and the final mapping f.sup.(n) and theenergy C.sup.(n)_(f) at the finest resolution are computed.Subsequently, after η is increased by a certain value Δη and the finalmapping f.sup.(n) and the energy C.sup.(n)_(f) at the finest resolutionare again computed. This process is repeated until the optimal value isobtained. η represents the stiffness of the mapping because it is aweight of ##EQU25##

When η is zero, D.sup.(n)_(f) is determined irrespective of the previoussubmapping, and the present submapping would be elastically deformed andbecome too distorted. When η is very large, D.sup.(n)_(f) is almostcompletely determined by the previous submapping. The submappings arethen very stiff, and the pixels are mapped to the same locations. Theresulting mapping is therefore an identity mapping. When the value of ηincreases starting from 0, C.sup.(n)_(f) gradually decreases asdescribed later. When it goes beyond the optimal value, however, theenergy begins to increase, as shown in FIG. 4. In FIG. 4, x-axisrepresents η, and y-axis represents C_(f).

The optimum value of η with the minimum C.sup.(n)_(f) is obtained inthis manner. However, different from the case of λ, various elementsaffect the computation relative to η, as a result of which C.sup.(n)_(f)varies while slightly fluctuating. This difference is caused because asubmapping is re-computed only once for each step in the case of λ,whereas all the submappings must be re-computed in the case of η.Whether or not the obtained value of C.sup.(n)_(f) is minimum cannot bejudged immediately because of the fluctuation. The true minimum has toagain be searched for in detail near the obtained candidate minimumvalue with a smaller interval.

[1.5] Supersampling

The range of f.sup.(m,s) can be expanded to R×R to increase the degreeof freedom when deciding the correspondence between the pixels. (Rstands for the set of real numbers.) In this case, the intensity valuesof the pixels of the destination image is interpolated to providef.sup.(m,s) with the intensity values at non-integer points ##EQU26##That is, supersampling is performed. In the implementation, f.sup.(m,s)is allowed to take integer and half integer values, and ##EQU27## isgiven by ##EQU28##

[1.6] Normalization of the Pixel Intensity of Each Image

When the source and destination images contain quite different objects,the raw pixel intensity may not be used to compute the mapping because alarge difference in the pixel intensity causes excessively large energyC.sup.(m,s) for the pixel intensity, and this makes it difficult toobtain accurate evaluation.

For instance, in a case where a human face and a face of a cat arematched as shown in FIG. 8, the face of the cat is furry and is amixture of very bright pixels and very dark pixels. In this case, tocompute the submappings of the subimages between the two facial images,it is preferred to normalize the subimages; i.e., the darkest pixelintensity should be set to 0, the brightest pixel intensity to 255, andthe other pixel intensity values are linearly interpolated.

[1.7] Implementation

In the implementation, a heuristic where the computation proceedslinearly as the souse image is scanned is used. First, the value off.sup.(m,s) is determined at the top leftmost pixel (i,j)=(0,0). Thevalue of each f.sup.(m,s) (i,j) is then determined while i is increasedby one at each step. When i reaches the width of the image, j isincreased by one and i is set to zero. In this way, f.sup.(m,s) (i,j) isdetermined while scanning the source image. Once pixel correspondence isdetermined for all the points, one mapping f.sup.(m,s) is determined.

When a corresponding point q_(f)(i,j) is determined for P.sub.(i,j), acorresponding point q_(f)(i,j+1) of p.sub.(i,j+1) is next determined.The position of q_(f)(i,j+1) is constrained by the position ofq_(f)(i,j) as the former satisfies the BC. Thus, in this system, a pointwhose corresponding point is determined earlier has higher priority. Ifthe situation in which (0,0) is always given the highest prioritycontinues, the mapping to be finally obtained might be unnecessarilybiased. In order to avoid this, f.sup.(m,s) is determined as follows inthis embodiment.

When (s mod 4) is 0, f.sup.(m,s) is determined starting from (0,0) whileincreasing i and j. When (s mod 4) is 1, it is determined starting fromthe top rightmost location while decreasing i and increasing j. When (smod 4) is 2, it is determined starting from the bottom rightmostlocation while decreasing i and j. When (s mod 4) is 3, it is determinedstarting from the bottom leftmost location while increasing i anddecreasing j. Since no conception of submappings, i.e., parameter s,exists in the finest n-th level, computation is done consecutively intwo directions assuming s=0 and s=2.

In the actual implementation, the values of f.sup.(m,s) (i,j) thatsatisfy the BC are chosen from the candidates (k,l) by awarding apenalty to the candidates that violate the BC. The energy D.sub.(k,l) ofthe candidate that violates the third condition is multiplied by φ andthat of a candidate that violates the first or second condition ismultiplied by ψ. In the implementation, φ=2 and ψ=100000 are used.

For the above checking of the BC conditions, the following test isperformed when determining (k,l)=f.sup.(m,s) (i,j). That is, for eachgrip point (k,l) in the inherited quadrilateral of f.sup.(m,s) (i,j),whether or not the z-component of the outer product of

    W=A×B                                                (39)

is equal to or greater than 0 is checked, wherein ##EQU29## (here, thevectors are regarded as 3D vectors and the z-axis is defined in theorthogonal right hand coordinate system.) When W is negative, thecandidate is awarded a penalty by multiplying D.sup.(m,s).sub.(k,l) by ψto avoid choosing, if possible.

FIGS. 5(a) and 5(b) show the reason why this condition is considered indetection. FIG. 5(a) represents a candidate without a penalty; FIG. 5(b)represents one with a penalty. When determining the mapping f.sup.(m,s)(i,j+1) for the adjacent pixel at (i,j+1), there is no pixel on thesource image plane that satisfies the BC if the z-component of W isnegative because q.sup.(m,s).sub.(k,l) exceeds the boundary of theadjacent quadrilateral.

[1.7.1] The Order of Submappings

In the implementation, σ(0)=0, σ(1)=1, σ(2)=2, σ(3)=3, σ(4)=0 have beenused at the even levels of resolution, and σ(0)=3, σ(1)=2, σ(2)=1,σ(3)=0, σ(4)=3 have been used at the odd levels of resolution. Thus, thesubmappings are shuffled. Note that submapping are originally of fourtypes, and s may be any of 0 to 3. In the implementation, however,processing for s=4 is performed for the reason to be described later.

[1.8] Interpolations

After the mapping between the source and destination images isdetermined, the intensity values of the corresponding pixels areinterpolated. In the implementation, trilinear interpolation is used.Suppose a square p.sub.(i,j) p.sub.(i+1,j) p.sub.(i,j+1) p.sub.(i+1,j+1)on the source image plane is mapped to a quadrilateral q^(f).sub.(i,j)q^(f).sub.(i+1,j) q^(f).sub.(i,j+1) q^(f).sub.(i+1,j+1) on thedestination image plane. For simplicity, the distance between the imageplanes is assumed to be 1. The intermediate image pixels r(x,y,t)(0≦x≦N-1, 0≦y≦M-1) whose distance from the source image plane is t(0≦t≦1) are obtained as follows. First, the location of the pixelr(x,y,t), wherein x,y,t.di-elect cons.R, is determined by the equation##EQU30## The value of the pixel intensity at r(x,y,t) is thendetermined by the equation ##EQU31## wherein dx and dy are parametersvarying from 0 to 1.

[1.9] Constraining the Mapping

In the above determination of the mapping, no designation concerningpixel correspondence between the source and the destination images isgiven from outside. However, when correspondence between particularpixels of the two images is input beforehand, the mappings can bedetermined using the correspondence as a constraint.

The basic idea is to roughly distort the source image by the approximatemapping that maps the specified pixels of the source image to thespecified pixels of the destination images, and then compute theaccurate mapping f.

First, the specified pixels of the source image are mapped to thespecified pixels of the destination image to determine the approximatemapping that maps other pixels of the source image to appropriatelocations. Pixels in the vicinity of the specified pixel are mapped tothe locations near the position where the specified one is mapped. Theapproximate mapping at the m-th level in the resolution hierarchy isdenoted as F.sup.(m).

The approximate mapping F is determined as follows. First, the mappingfor several pixels are specified. When n_(s) pixels

    P(i0,j0),P(i1,j1), . . . , P(i.sub.n.sbsb.s -1,j.sub.n.sbsb.s -1)(44)

of the source image are to be specified, the values ##EQU32## aredetermined.

For the remaining pixels of the source image, the amount of displacementis the weighted average of the displacement of P.sub.(ih,jh) (h=0, . . ., n_(s) -1); i.e., a pixel P.sub.(i,j) is mapped to the pixel of thedestination image at ##EQU33##

Second, the energy D.sup.(m,s).sub.(i,j) of the candidate mappings f ischanged so that mappings f similar to F.sup.(m) have lower energy. To beprecise, ##EQU34## and κ, ρ≧0. Finally, the automatic computing processof mappings described before determines f completely.

Note that E₂.sup.(m,s).sub.(i,j) becomes 0 if f.sup.(m,s) (i,j) issufficiently close to F.sup.(m) (i,j) i.e., within ##EQU35##

It is defined so because it is desirable to determine each valuef.sup.(m,s) (i,j) automatically to fit in an appropriate place in thedestination image so long as it is close to F.sup.(m) (i,j). Because ofthis, it is unnecessary to specify the precise correspondence in detail;the source image is automatically mapped so that it matched thedestination image.

[2] Concrete Processing Procedure

The flow of the process using the respective elemental techniquesdescribed in [1] will be described.

FIG. 6 is the flowchart of the entire procedure of this embodiment.Referring to this drawing, processing using a multiresolutional criticalpoint filter is first performed (S1). A source image and a destinationimage are then matched (S2). S2 is not always necessary for the presentinvention, and other processing, such as image recognition, may beperformed instead, based on the characteristics of the image obtained atS1.

FIG. 7 is a flowchart showing the details of the process at S1 in FIG.6. This process is performed on the assumption that a source image and adestination image are matched at S2. To be specific, a source image isfirst hierarchized using a critical point filter (S10) to obtain aseries of source hierarchical images. Then, a destination image ishierarchized in the same way (S11) to obtain a series of destinationhierarchical images. The order of S10 and S11 in the flow is arbitrary,and they may be performed in parallel.

FIG. 8 is a flowchart showing the details of the process at S10 in FIG.7. The size of the original source image is defined 2^(n) ×2^(n). Sincesource hierarchical images are sequentially generated from a fineresolution to a coarse resolution, the parameter m which indicates thelevel of resolution to be processed is set at n (S100). Then, criticalpoints are detected from the images p.sup.(m,0), p.sup.(m,1),p.sup.(m,2), p.sup.(m,3), of the m-th level of resolution, using acritical point filter (S101) so that the images p^(m-1),0),p.sup.(m-1,1), p^(m-1),2), p.sup.(m-1,3) of the (m-1)th level aregenerated (S102). Since m=n here, p.sup.(m,0) =p.sup.(m,1) =p.sup.(m,2)=p.sup.(m,3) =p.sup.(n) holds, and four types of subimages are thusgenerated from a single source image.

FIG. 9 shows correspondence between partial images of the m-th and(m-1)th levels of resolution. Respective values in the drawing representthe intensity of respective pixels. p.sup.(m,s) stands for any one ofthe four images p.sup.(m,0) to p.sup.(m,3), and is regarded asp.sup.(m,0) for the generation of p.sup.(m-1,0). From the block in FIG.9, comprising four pixels with their pixel intensity values showninside, images p.sup.(m-1,0), p.sup.(m-1,1), p.sup.(m-1,2), andp.sup.(m-1,3) obtain 3," "8," "6," and "10", respectively, according tothe rules described in [1.2]. This block at the m-th level is replacedat the (m-1)th level by respective single pixels obtained. Thus, thesize of the subimages at the (m-1)th level is 2^(m-1) ×2^(m-1).

After m is decremented (S103 in FIG. 8), it is ensured that m is notnegative (S104). The process returns to S101, where subimages of thenext level of resolution, i.e., a courser level by one, are generated.The above process is repeated until subimages at m=0, i.e., at the 0-thlevel, are generated to complete the process at S10. The size of thesubimages at the 0-th level is 1×1.

FIG. 10 shows source hierarchical images for n=3 generated at S10. Theinitial source image is the sole image which is common to the fourseries. The following four types of subimages are generatedindependently, depending on the type of a critical point. Note that theprocess in FIG. 8 is common to S11 in FIG. 7, and that destinationhierarchical images are generated through the same procedure. With theabove, the process at S1 in FIG. 6 is completed.

Upon completion of the process at S1, preparation for matchingevaluation is made before proceeding to S2 in FIG. 6. FIG. 11 shows thepreparation procedure. To be specific, a plurality of evaluationequations are determined (S30), namely the energy C.sup.(m,s)_(f)relative to a pixel value, introduced in [1.3.2.1], and the energyD.sup.(m,s)_(f) relative to the smoothness of the mapping introduced in[1.3.2.2]. By combining these equations, a combined evaluation equationis determined as λC .sup.(m,s)_(f) +D.sup.(m,s)_(f) (S31). Using ηintroduced in [1.3.2.2], a combined evaluation equation is denoted

    ΣΣ(λC.sup.(m,s).sub.(i,j) +ηE.sub.0.sup.(m,s).sub.(i,j) +E.sub.1.sup.(m,s).sub.(i,j))(52)

The sum is computed respectively for i and j being 0, 1 . . . , 2^(m)-1. With the above, preparation for matching evaluation is completed.

FIG. 12 is a flowchart showing the details of the process at S2 in FIG.6. As described in [1], source hierarchical images and destinationhierarchical images are matched between images at the same level ofresolution. For detecting global corresponding correctly, a matching iscalculated at from a coarse level to a fine level of resolution. Since acritical point filter is used in generation of source and destinationhierarchical images, the location and intensity of critical points areclearly preserved even at a coarse level. This makes it possible toseize global correspondence more accurately than by a conventionalmethod.

Referring to FIG. 12, coefficient parameter η and level parameter m areset at 0 (S20). A matching is computed between respective four subimagesat the m-th level of the source hierarchical images and of thedestination hierarchical images so that four types of submappingsf.sup.(m,s) (s=0, 1, 2, 3) which satisfy the BC and minimize the energyare obtained (S21). The BC is checked using an inherited quadrilateraldescribed in [1.3.3]. The submappings at the m-th level are constrainedby those at the (m-1)th level, as indicated by equations 17 and 18. Thematching calculated at a coarser level of resolution is thussequentially used in subsequent calculation of a matching. This may becalled vertical reference between different levels. When m=0, there isno courser level and the process therein should be different from theprocess, which will be described later referring to FIG. 13.

Horizontal reference is also performed at the same level. As indicatedby equation 20 in [1.3.3], and f.sup.(m,3), f.sup.(m,2), f.sup.(m,1) arerespectively defined so as to be analogous to f.sup.(m,2), f.sup.(m,1),and f.sup.(m,0) because it is unnatural that submappings relative todifferent types of critical points are completely different from oneanother so long as they are originally included in the same source ordestination images. As is known from equation 20, when submappings arecloser to each other, the energy becomes smaller and the matching isconsidered as more preferable.

As for f.sup.(m,0), which is to be initially determined, a coarser levelby one is referred to since there is no other submapping at the samelevel available for reference. In the implementation, however,f.sup.(m,0) is determined after the submappings were obtained up tof.sup.(m,3), by updating f.sup.(m,0), initially determined using thesubmapping f.sup.(m,3) as a constraint. This process is equivalent tothe process in which s=4 is substituted into equation 20 to makef.sup.(m,4) final f.sup.(m,0). The above process is employed to avoidthe tendency that the relevance between f.sup.(m,0) and f.sup.(m,3)becomes too low. This scheme actually produced a preferable result. Inaddition to the above, the submappings are shuffled in theimplementation as described in [1.7.1] so as to maintain close relevanceamong submappings which are originally determined independently forevery type of critical point. Further, in order to prevent theprocessing from being biased due to repeated use of the same startingpoint, the location thereof is changed according to the value of s, asdescribed in [1.7].

FIG. 13 shows the way how the submapping is determined at the 0-thlevel. At the 0-th level, an identity mapping is automatically selectedfor each of the four submappings f.sup.(m,s) since each of f.sup.(m,s)is constituted of a single pixel. FIG. 14 shows the way how thesubmappings are determined at the first level. At the first level, eachof the submappings is constituted of four pixels, which are indicated bya solid line in the drawing. A corresponding point (pixel) of the point(pixel) x in p.sup.(1,s) is searched within q.sup.(1,s) according to thefollowing procedure.

1. An upper left point a, an upper right point b, a lower left point, alower right point d of the point x are searched at the first level ofresolution.

2. Pixels to which the points a to d belong at a coarser level by one,i.e., the 0-th level, are searched. In FIG. 14, the points a to d belongto the pixels A to D, respectively. The points A to C are hypotheticalpixels which only exist virtually.

3. The corresponding points A' to D' of the pixels A to D, which havealready been defined at the 0-th level, are plotted in q.sup.(1,s). Thepixels A' to C' are also hypothetical pixels and assumed to be locatedat the same positions as the pixels A to C.

4. Presuming that the corresponding point a' of the point a in the pixelA is located inside the pixel A', the point a' is accordingly plottedunder the assumption that the point a occupies the same location in thepixel A as that of the point a in the pixel A (lower right here).

5. The corresponding points b' to d' are plotted using the same methodas the above so as to constitute an inherited quadrilateral using thepoints a' to d'.

6. The corresponding point x' of the point x is searched such that theenergy becomes minimized in the inherited quadrilateral. Candidatecorresponding points x' may be limited to the pixels, for instance,whose centers are included in the inherited quadrilateral. In FIG. 14,four pixels all become candidates.

The above described is a procedure for determining the correspondingpoint of a given point x. The same processing is performed to all theother points for determination of the submappings. As a deformedinherited quadrilateral is expected at the second and upper levels, thepixels A' to D' are located, getting apart from one another as shown inFIG. 3.

Once four submappings at the m-th level are determined as describedabove, m is incremented (S22 in FIG. 12). It is then ensured that m doesnot exceed n (S23) before the process returns to S21. Hereinafter, everytime the process returns to S21, submappings at a finer level ofresolution are obtained until the process finally returns to S21 wherethe mapping f.sup.(n) at the n-th level is determined. The above mappingis denoted as f.sup.(n) (η=0), as it has been determined relative toη=0.

Next, to obtain the mapping for the next η, η is shifted by Δη and m isreset to zero (S24). After ensuring that new η does not exceed apredetermined threshold η_(max) (S25), the process returns to S21, wherethe mapping f(η) (η=Δη) for the new η is obtained. This process isrepeated to obtain f.sup.(n) (η=iΔη) (i=0, 1, . . . ) at S21. When ηexceeds η_(max), the process proceeds to S26, where the optimalη=η_(opt) is determined using a method described later, and f.sup.(n)(η=η_(opt)) becomes the final mapping f.sup.(n).

FIG. 15 is a flowchart showing the details of the process at S21 in FIG.12. According to this flowchart, the submappings at the m-th level aredetermined for a certain η. In determination of the mappings, theoptimal λ is defined independently for every submapping in thisembodiment.

Referring to FIG. 15, s and λ are reset to zero (S210). Then, thesubmapping f.sup.(m,s) that minimizes the energy is obtained for then λ(and implicitly for then η) (S211), and denoted as f.sup.(m,s) (λ=0). Toobtain a mapping for the next λ, λ is shifted by Δλ (S212), and it isensured that it does not exceed a predetermined threshold λ_(max)(S213). The process then returns to S211, where f.sup.(m,s) (λ=iΔλ)(i=0,1, . . . ) is obtained. When λ exceeds λ_(max), the processproceeds to S214 to determine the optimal λ=λ_(opt) and f.sup.(m,0)(λ=λ_(opt)) becomes the final mapping f.sup.(m,0) (S214).

Subsequently, λ is reset to zero and s is incremented to obtain the nextsubmapping at the same level (S215). After ensuring that s does notexceed 4 (S216), the process returns to S211. When s=4, f.sup.(m,0) isupdated reflecting f.sup.(m,3) as described above to completedetermination of submappings at that level.

FIG. 16 shows the behavior of the energy C.sup.(m,s)_(f) correspondingto f.sup.(m,s) (λ=iΔλ) which has been obtained as to certain m and swhile changing λ. As described in [1.4], as λ increases, C.sup.(m,s)_(f)normally decreases and turns to increase when λ exceeds the optimalvalue. In this embodiment, λ with minimum C.sup.(m,s)_(f) is determinedas λ_(opt). Even if C.sup.(m,s)_(f) becomes smaller again in the rangeλ>λ_(opt), the mapping has already been spoiled by then. For thisreason, the first minimum should be noticed. λ_(opt) is independentlydetermined for every submapping including for f.sup.(n).

FIG. 17 shows the behavior of the energy C.sup.(n)_(f) corresponding tof.sup.(n) (η=iΔη) which has been obtained while changing η. Here again,since C.sup.(n)_(f) normally decreases as η increases and turns toincrease when it exceeds the optimal value, η with minimum C.sup.(n)_(f)is determined as η_(opt). FIG. 17 can be taken as an enlarged diagramaround zero along the horizontal axis in FIG. 4. once η_(opt) isdecided, f.sup.(n) can be finally determined.

As described above, this embodiment can provide various merits. First,since edge detection is unnecessary, problems occurring in connectionwith the conventional technique of edge detection type can be solved.Further, prior knowledge about objects included in an image isunnecessary, and automatic detection of corresponding points isachieved. Using a critical point filter, it is possible to preserveintensity and locations of critical points even at a coarse level ofresolution. This has strong application to object recognition,characteristic extraction, and image matching. As a result, it ispossible to construct an image processing system which significantlyreduces manual labors.

Some extensions or modifications of the above embodiment may be made asfollows:

i) Parameters are automatically determined while matching source anddestination hierarchical images in the above embodiment. This method canbe applied not only to matching hierarchical images but also to matchingtwo images.

For instance, energy E₀ relative to a difference in the intensity ofpixels and energy E₁ relative to a positional displacement of pixelsbetween two images may be used as evaluation equations, and a linear sumof these equations, i.e., E_(tot) =E₀ +E₁, may be used as a combinedevaluation equation. While noting or paying attention to theneighborhood of the extreme of this combined evaluation equation, α isautomatically determined. That is, mappings which minimize E_(tot) areobtained for various α. α with minimum E₁ concerning α is determined asan optimal parameter α_(opt), and the mapping corresponding to α_(opt)among various mappings obtained above is finally determined as theoptimal mapping between those images.

Many other methods are available for determination of evaluationequations. For instance, an equation which becomes larger for a betterestimation, such as 1/E₁ and 1/E₂, may be employed. A combinedevaluation equation is not necessarily a linear sum, and an n-fold sum(n=2, 1/2, -1, -2, etc.), a polynomial, an arbitrary function may beemployed as desired.

The system may employ a single parameter such as the above α, twoparameters such as η and λ or more parameters. When multiple parametersare used, they are determined while changing one by one.

ii) In the described embodiment, a parameter is determined in theprocess in which the mapping is first determined such that the value ofa combined evaluation equation is minimized, and the point at which oneof the evaluation equations constituting the combined evaluationequation, namely, C.sup.(m,s)_(f), is minimized is then detected.

However, instead of this two-step processing, a parameter may beeffectively determined in some cases simply such that the minimum of acombined evaluation equation becomes the smallest. In such a case, αE₀+βE₁, for instance, may be employed as a combined evaluation equation,and α+β=1 is imposed as a constraint for equal treatment of respectiveevaluation equations. The essence of automatic determination of aparameter lies in so determining the parameter that it minimizes theenergy.

iii) In the above embodiment, four types of submappings are generatedfor four types of critical points at respective levels of resolution.However, one, two, or three types out of the four may be selectivelyused. For instance, if only one bright point exists in an image,generation of hierarchical images based solely on f.sup.(m,3), which isrelative to a maximum, can produce sufficient effect. In this case, noother submapping is necessary at the same level, and the amount ofcomputation relative to s is effectively reduced.

iv) In the above embodiment, as the level of resolution of an image isadvanced by one through a critical point filter, the number of pixelsbecomes 1/4. However, assuming that one block is consisted of 3×3pixels, and critical points are searched in this 3×3 block, the numberof pixels is reduced to 1/9 as the level advances by one.

v) When the source and the destination images are color images, they arefirst converted to monochrome images, and the mappings are thencomputed. The original color images are then transformed by theresulting mappings. Other methods may include the computation ofsubmapping for respective RGB component.

[4] Experimental Results

Using the described embodiment, various images can be interpolated. Whentwo images from different viewpoints are interpolated, images fromintermediate viewpoints can be generated. This has strong applicationsfor World Wide Web (WWW) based information servers because it enablesgenerating arbitrary views from a limited number of images. When imagesof two persons faces are interpolated, it is possible to performmorphing. When the images are cross-sections of 3D objects such as CTand MRI data, the interpolation enables to reconstruct accurate 3Dobject shapes for volume rendering.

FIGS. 18(a), 18(b), and 18(c) relate to a case where the mapping is usedfor generating intermediate view point images. A left-eye image and aright-eye image are interpolated here. FIG. 18(a) shows the source imageviewed with a left eye; FIG. 18(b) shows that with a right eye; and FIG.18(c) shows the resulting intermediate image wherein the value of theparameter t described in [1.8] is 0.5 for simplicity.

FIGS. 19(a), 19(b), and 19(c) relate to a case where the mapping is usedfor morphing of human faces. Two facial images of different persons areinterpolated. FIG. 19(a) shows the source image; FIG. 19(b) shows thedestination image; FIG. 19(c) shows the source image superimposed by thedestination image; and FIG. 19(d) shows the resulting intermediate imagewherein t=0.5.

FIGS. 20(a), 20(b), and 20(c) relate to a case where the mapping is usedfor interpolation of a human face and a face of a cat. FIG. 20(a) showsa face of a cat; FIG. 20(b) shows a morphing image of a human face and aface of a cat. FIG. 19(a) is used for the human face. The normalizationof the pixel intensity described in [1.6] is used only in this example.

FIGS. 21(a), 21(b), and 21(c) relate to a case where the system isapplied to images including a number of objects. FIG. 21(a) shows thesource image; FIG. 21(b) shows the destination image; and FIG. 21(c) isthe resulting intermediate image wherein t=0.5.

FIGS. 22(a), 22(b), 22(c), and 22(d) show the result where the mappingis used for interpolating images of a human brain whose cross sectionsare obtained by MRI. FIG. 22(a) shows the source image; FIG. 22(b) showsthe destination image (the upper cross section); FIG. 22(c) shows theintermediate image wherein t=0.5; and FIG. 22(d) shows the oblique viewof the result of the volume rendering with four cross sections. Theobject is completely opaque and the interpolated pixels whose intensityis larger than 51(=255×0.2) are displayed. The reconstructed object isthen cut vertically near the center to show the interior of the volume.

In these examples, an MRI image is of 512×512 pixels, and other imagesare of 256×256 pixels. The intensity of a pixel varies from 0 to 255.The SJ condition describe d in [1.3.1] is used in all the applicationexamples except for FIGS. 21(a) to 21(c). In all the applicationexamples, B_(0thres) =0.003 and B_(1thres) =0.5 are used, and it has notbeen necessary to modify their values for any image. The pixel intensityof each subimages has been normalized in FIGS. 20(a) and 20(b) only.

The writing of this specification necessitated choosing or creating anumber of terms and it should be noted that these choices were made inorder to describe the current invention, and not to limit it from thefull scope and spirit as claimed.

What is claimed is:
 1. A multiresolutional filtering method comprising:adetection step of detecting critical points through a two dimensionalsearch carried out inside each of a plurality of blocks constituting afirst image; and a generation step of generating a second image having alower resolution than that of the first image through extraction of thedetected critical points.
 2. A method as defined in claim 1, whereineach of the detected critical points is detected by comparing pixelvalues within said each of the blocks.
 3. A method as defined in claim2, whereina pixel having a maximum pixel value in a first direction anda second direction of the two-dimensional search is detected as amaximum.
 4. A method as defined in claim 2, whereina pixel having aminimum pixel value in a first direction and a second direction of thetwo-dimensional search is detected as a minimum.
 5. A method as definedin claim 1, whereina critical point detected inside each of the blocksis chosen to represent the block in a second image to thereby reduceresolution of the second image relative to the first image.
 6. Themethod of claim 1, wherein each of the blocks has an identical shape. 7.The method of claim 6, wherein each of the blocks has identicaldimensions.
 8. The method of claim 7, further comprising selecting onepixel from each of the blocks, wherein the second image is formed fromthe selected pixels.
 9. A multiresolutional filtering methodcomprising:a detection step of dividing a first image into a pluralityof blocks and detecting critical points through a two-dimensional searchcarried out in each of the blocks; and a generation step of generating asecond image having a lower resolution than the first image throughextraction of the detected critical points, wherein the critical pointsare saddle points, wherein each of the saddle points has a value that isa maximum in a first direction of the two-dimensional search and aminimum in a second direction of the two-dimensional search.
 10. Amultiresolutional filterng method comprising:dividing a first image intoa plurality of blocks, wherein each of the blocks includes four pixelsconsisting of two pixels in a horizontal direction and two pixels in avertical direction; detecting a critical point through a two-dimensionalsearch carried out in each of the blocks, wherein the detected criticalpoint in said each of the blocks is detected by comparing pixel valueswithin said each of the blocks; and generating a second image having alower resolution than the first image through extraction of the detectedcritical points, wherein each of the four pixels in said each of theblocks is classified into one of a maximum, a minimum and one of twotypes of saddle points.
 11. A multiresolutional filtering methodcomprising:a detection step of dividing a first image into a pluralityof blocks and detecting critical points through a two-dimensional searchcarried out in each of the blocks; and a generation step of generatingsecond images having a lower resolution than the first image throughextraction of the detected critical points, wherein one of the secondimages is generated for each critical point te detected inside said eachof the blocks.
 12. The method of claim 11, wherein the detected criticalpoints are one of a maximum critical point type, a minimum criticalpoint type and a saddle point critical point type, and wherein a pixelin each of said blocks having a minimum pixel value in a first directionand a second direction of the two-dimensional search is detected as theminimum critical point type, the pixel having a maximum pixel value inthe first and second directions is detected as the maximum criticalpoint type and the pixel having the maximum pixel value in the firstdirection and the minimum pixel value in the second direction isdetected as the saddle point critical point type.
 13. Amultiresolutional filtering method, wherein a critical points aredetected in a first image by performing a two dimensional search in eachof a plurality of blocks, and a second image having a lower resolutionthan that of the first image is generated with the detected criticalpoints, wherein the detected critical points are one of a maximum, aminimum and a saddle point.
 14. A multiresolutional filtering method,comprising:a detection step of detecting critical points through atwo-dimensional search carried out on a first image; and a generationstep of generating a plurality of second images based on said criticalpoints, wherein the plurality of second images each has a lowerresolution than said first image.
 15. The method of claim 14, whereineach of the critical points are only one of a set of critical pointtypes, wherein the set of critical point types includes a maximumcritical point type, a minimum critical point type, a first saddle pointtype and a second saddle point type, and wherein each of the pluralityof second images is formed using only one corresponding critical pointtype of the set of critical point types.
 16. A multiresolutionalfiltering method, comprising:detecting critical points through a twodimensional search carried out in each of a plurality of blocksconstituting a designated image; generating a current image having alower resolution than that of the designated image using the detectedcritical points; and setting the current image to be the designatedimage; and repeating the detecting, generating and setting steps. 17.The method of claim 16, wherein each of the blocks has an identicalshape.
 18. The method of claim 16, further comprising outputting thecurrent image when a resolution of the current image is not greater thana prescribed resolution.
 19. The method of claim 16, wherein thedetected critical points are only one of a maximum critical point type,a minimum critical point type and a saddle point critical point type,and wherein a pixel in each of said blocks having a minimum pixel valuein a first direction and a second direction of the two-dimensionalsearch is detected as the minimum critical point type, the pixel havinga maximum pixel value in the first and second directions is detected asthe maximum critical point type and the pixel having the maximum pixelvalue in the first direction and the minimum pixel value in the seconddirection is detected as the saddle point critical point type.